value-based brand exploitation strategy to grow firm value

ldquoValue-Based Brand Exploitation Strategy to Grow Firm Valuerdquo copy 2018 Marc Fischer Max

Backhaus and Tobias Hornig Report Summary copy 2018 Marketing Science Institute

MSI working papers are distributed for the benefit of MSI corporate and academic members

and the general public Reports are not to be reproduced or published in any form or by any

means electronic or mechanical without written permission

Marketing Science Institute Working Paper Series 2018 Report No 18-127

Value-Based Brand Exploitation Strategy to Grow Firm

Value

Marc Fischer Max Backhaus and Tobias Hornig

Report Summary

Recent meta-analysis suggests that on average improving brand equity by 10 translates to an

increase of firm value of 33 Brand-driven value creation arises from growth andor return

relative to capital cost Thus firms may seek to leverage brand strength for driving the profit

margin minus eg through extending the price premium and lowering the cost of selling minus or it may

focus on exploiting brand equity to grow the business minus eg by expanding into new markets

Which strategy is better-suited to drive value creation is not evident Little is known about the

mechanisms that underlie brand-driven value creation and how these might differ across firms

and industries

The Study

In this report Marc Fischer Max Backhaus and Tobias Hornig undertake an approach based on

analysis of financial data to understand the role of customer-based brand equity (CBBE)

measures in creating firm value via three drivers (1) investorsrsquo expectations about the rate of

return on new invested capital (2) the future growth in earnings and (3) the length of the period

during which a firm can earn excess return

They econometrically estimate the CBBE effects on these value drivers at the firm level in a

broad sample of 613 firms covering a period of nine years from 2005 to 2013 Their database

includes retailers durable and non-durable products as well as services

They find that while earnings growth and sustainability of excess return are the most influential

drivers for most industries and firms the return on invested capital is more relevant in industries

such as media information technology and industrial and utilities They also find substantial

across-firm and across-industry variation in both CBBE leverage effects and relative importance

of value drivers Finally they identify cases where CBBE impact on a value driver is not well

aligned with its relevance for value generation leaving an untapped potential for value creation

Put into Practice

Marketers should think differently about their brand strategy a detailed analysis of each firmrsquos

situation is necessary to determine the most effective brand exploitation strategy It is not

sufficient to focus only on raising CBBE by investing into brand building It is equally if not more

important to think about the best brand exploitation strategy so that brand investments are not

wasted but appropriate value in the marketplace

Marc Fischer is Professor of Marketing and Market Research University of Cologne and

Professor of Marketing University of Technology Sydney Max Backhaus is Associate

Researcher University of Cologne Tobias Hornig is Project Manager BI Siemens Industry

Software GmbH

Marketing Science Institute Working Paper Series 1

According to the theory of efficient capital markets all available information about a

company is incorporated into its stock price (Fama 1970) The stock price rises if unexpected

new information arrives that leads investors to increase expectations regarding future cash flows

and vice versa But what happens when investors learn about an increase in brand equity Extant

research (eg Bharadwaj et al 2011 Mizik and Jacobson 2008 Srinivasan and Hanssens 2009)

shows that this event is likely to result in higher firm value Edeling and Fischerrsquos (2016) recent

meta-analysis suggests that on average improving brand equity by 10 translates to an increase

of firm value of 33 A self-evident implication of this empirical regularity is that brand

managers should strive to improve their brandrsquos equity rating which can be regularly monitored

by employing established customer-based brand equity (CBBE) measures such as EquiTrend

(Harris Interactive) BAV (Young amp Rubicam) and YouGovrsquos brand index

While intuitive however this conclusion might be too simple Consider the two US

retailers CVS Health and Kroger Data from 2005ndash2013 indicate the firms are of similar size in

terms of average sales US$885 billion for CVS and US$774 billion for Kroger a difference of

14 The strength of their brands is also not very different The CBBE (EquiTrend) rating in

2005ndash2013 averaged 666 for CVS and 609 for Kroger a mere 9 difference However we

observe a striking gap between the average market capitalization of the two firms CVS was

valued three times higher than Kroger (US$470 vs US$156 respectively a difference of

200) In 2015 Brand Finance valued the CVS brand at US$203 billion compared with US$60

billion for the Kroger brand a 243 difference It is not apparent why two firms that are from the

same industry sector and are comparably strong CBBE and sales show such substantial

differences in brand and firm valuation Deeper analysis of financial data is likely to provide

more insights In this paper we argue that a firmrsquos brand exploitation strategy is another possibly

complementary explanation


Valuation theory posits and practice bears out that value basically arises from two sources

return relative to capital cost and growth (Copeland et al 2013 Koller et al 2015) Consequently

a firm may either leverage the strength of its brand mainly to drive the profit margin (eg

through extending the price premium and lowering the cost of selling) or focus more on

exploiting brand equity to grow the business (eg by expanding into new markets) The right

choice however is not clear-cut the alternatives are not necessarily equal in their potential to

drive firm value It requires a deeper understanding about how firm value is generated as well as

about the firmrsquos specific situation and industry Unfortunately our knowledge about the

underlying mechanisms that cause brands to generate value is limited

Financial valuation theory (eg Copeland et al 2013) decomposes value creation from

return and growth into four value drivers the return on invested capital (ROIC) the cost of

capital (WACC) the earnings growth rate (EGR) and the sustainability of excess return (S)

First ROIC obtained by dividing after-tax operating profit by invested capital measures the

average rate of return on new investments that the firm expects to generate from its future

projects Second WACC reflects the capital-structure weighted average of the cost of equity and

cost of debt Third EGR represents the expected average rate by which earnings grow (note

however that this growth only adds value when producing excess return [ie ROIC ndash WACC gt

0) A fundamental assumption of competitive theory is that excess return cannot be maintained

forever (Demsetz 1982) Finally S measures the length of this period and is the capital market

equivalent of competitive advantage

The value contribution of these drivers is not equal rather it depends on industry and firm

characteristics To ensure that the firm follows the most effective route of value generation from

exploiting the strength of their brands it is necessary to know the leverage effect of CBBE on


these value drivers and which driver is most relevant Identifying this effect is the key substantive

contribution of the current research

Specifically we model the impact of CBBE on the financial value drivers and address the

following research questions

bull How large is the impact (measured as elasticity) of CBBE on each of

the value drivers

bull Which source of value creation growth or return is more relevant in

which industry during our observation period

bull How does the impact of CBBE value drivers vary across firms and

industries How does this difference resonate with the variation in

importance of value drivers for value generation

We answer these questions by analyzing a broad sample of 613 firms covering a period of

nine years (2005ndash2013) across a wide range of industries Our database includes retailers durable

and nondurable products and services Although a healthy body of research on the role of brands

for value generation exists we cannot use it to answer our questions for two main reasons First

we are not aware of studies that quantify the impact of brands on expected earnings growth and

the sustainability of excess return which are key value drivers Second we aim to estimate the

impact of CBBE on all value drivers together and for each individual firm Only then can we can

identify the strongest link of brand-driven value generation

Our study offers new insights for both scholars and practitioners We extend the branding

literature by demonstrating that how firms strategically exploit the strength of their brands makes

a difference We also contribute to the literature by introducing and studying the sustainability of

excess return which quantifies an important but unobserved construct competitive advantage

For managers our study suggests they should think differently about their brand strategy It is not

sufficient to focus only on raising CBBE by investing in brand building it is equally if not more


important to think about the best brand exploitation strategy so that brand investments are more

likely to appropriate value in the marketplace

We report findings that are new to the literature and highly informative for practice While

earnings growth and sustainability of excess return are the most influential drivers for most

industries and firms the return on invested capital is more relevant in industries such as media

information technology and industrial products and utilities Our results also offer a possible

solution to the puzzle of our introductory example We find that EGR is more important than

ROIC in driving firm value for both CVS Health and Kroger CVSrsquos brand exploitation strategy

is consistent with this ranking the leverage effect of its CBBE on EGR is much greater than on

ROIC and beats the industry average In contrast Krogerrsquos CBBE impact on ROIC exceeds the

industry average but fails with respect to EGR for which its leverage effect is below industry

average and 10 times lower compared with CVS Thus Krogerrsquos strength in brand exploitation

does not appear to align well with the relevance of value drivers for value generation

The remainder of the paper is structured as follows In the next section we present results of

interviews with industry experts summarize the related empirical literature and continue with a

discussion of brand mechanisms for value creation We then develop our modeling framework to

decompose firm value and specify the estimation equations The following section reports

information about the data sample and estimation issues It is followed by a discussion of results

We conclude the paper with implications for further research


Background

We first review the basic approaches to corporate valuation and then summarize the results

from interviews with industry experts We conclude this section by reviewing the related brand

literature

Corporate Valuation

Many approaches to company valuation exist It is beyond our scope to review the corporate

valuation literature in detail (for thorough reviews see eg Damodaran 2012 Koller et al

2015) According to Damodaran (2012) valuation approaches can be broadly categorized into

two classes direct and relative valuation Relative valuation refers to the multiplier analysis in

which a set of similar companies is identified and their market value linked to a common

performance metric such as sales or earnings before interest and taxes (EBIT) Although widely

used in practice relative valuation can be challenging because it may be difficult to find a set of

firms that is comparable to the focal firm

Direct valuation follows the framework of discounted cash flow (DCF) analysis Here the

idea is to estimate the intrinsic value of a company on the basis of its fundamentals It involves a

projection of future cash flows that are discounted at an appropriate rate that reflects the firmrsquos

risk and capital structure This type of valuation is attractive from both theoretical and practical

points of view It requires being explicit about the input information for cash flow projections and

coincides with the market value of a firm at least in theory Most importantly it is transparent

about the mechanism of value generation by incorporating the four key value drivers ROIC

WACC EGR and S (eg Copeland et al 2013 Koller et al 2015)

Industry Interviews

From November 2017 to January 2018 we conducted nine interviews with industry experts

The main purpose of these interviews was to understand the extent to which managers are


familiar with the mechanism of value generation and the role of brands in this system The

interviews lasted 20ndash30 minutes The interviewees were all senior executives with extensive

experience in several international markets (eg health care software energy business services

financial services) Two managers held senior marketing and sales positions four held senior

positions in finance and accounting one manager worked for investor relations and two

executives were chief executive officers (CEOs) and founder of a marketing analytics startup

company In each interview we first explained our framework of value generation that centers on

the four value drivers We then asked the interviewees to rank the value drivers EGR ROIC and

S according to their importance for value generation in their industry1 Finally we were

interested in understanding how they perceive the role of brands in influencing these value

drivers

Though we do not claim any representativeness the interviews produced important insights

First we observed no dominant perception about the relative importance of one financial driver

over others for value generation Three managers put ROIC first while four chose EGR Two

managers did not provide a ranking Surprisingly sustainability of excess return was considered

the least powerful driver The interviewees viewed the leverage effect of CBBE on the value

drivers as more differentiated Seven of the nine believed the effect depends to a great extent on

the firmrsquos brand exploitation strategy and market conditions such as the level of market

saturation and the intensity of competition Interestingly two executives pointed out that the key

role of brands is to secure competitive advantage (ie the largest brand impact should be on the

sustainability of excess return)

1 Interviewees did not consider WACC a relevant driver in their operations because it is mainly driven by

macroeconomic conditions and capital market factors that are beyond their control


In summary managers seem to understand that brands can fulfil various roles within the

mechanism of value generation However they exhibited no agreement on the relative

importance of brand leverage effects on value drivers In addition they perceived value drivers to

contribute differently to firm value depending on industry firm and time

Related Literature on the Value Relevance of Brands

The question of whether brands contribute to value generation has attracted the focus of a

large body of research within and beyond marketing Figure 1 provides an overview of the

relations and constructs that have been studied Note that because our focus is on CBBE we do

not include other brand equity measures (sales-based brand equity and financial brand equity)

here The literature can be summarized into two groups The first group of studies (eg Luo et al

2013 Mizik and Jacobson 2008) establishes evidence that brands are indeed valuable intangible

assets that contribute to shareholderfirm value The literature shows overwhelming support for

the value relevance of brands as emphasized by Edeling and Fischer (2016)

The second group of studies investigates the role of brands for individual components and

drivers of firm value which is also the focus of our study It is therefore important to position the

current study relative to prior empirical research on the impact of CBBE on value drivers which

we do in Table 1 As is evident from the table the majority of these studies (four of the seven)

focus on the relation between brands and risk factors Whereas strong brands appear to reduce the

cost of debt the findings on equity cost (ie systematic risk) are mixed Bharadwaj et al (2011)

find a positive relation but Rego et al (2009) report a negative relation In contrast the findings

on profitability are consistent and suggest that strong brands improve profitability

We were not able to find studies that measure the impact of CBBE on either EGR or S We

note Morgan et alrsquos (2009) work in which they investigate the relationship between brand

management capability and profit growth using a cross-sectional manager survey This study


shows no significant impact on profit growth However because the focal variable is

management capability and not CBBE the study measures a precursor or moderator effect which

is not the same as the direct effect of CBBE on EGR We conclude that the brand impact on EGR

and S is be underresearched and our study is aimed to fill this gap More importantly Table 1

shows that no prior research has investigated all value drivers together which is important to be

able to compare their relative importance for value generation

Valuation Theory and Mechanisms of Value Creation by Brands

In the following subsections we establish our theoretical framework of value drivers that are

direct outcomes of common DCF valuation (Copeland et al 2013) We work out the various

mechanisms by which brands create value for firms and how these mechanisms relate to the value

drivers

A Formula Approach to Corporate Valuation

In a discounted cash flow framework a firmrsquos value equals the present value of the expected

future cash flows When valuing a business these expected cash flows are usually generated from

estimated earnings in future periods which in turn are determined by current earnings and the

expected growth rate in these earnings (Koller et al 2015) Thus firm value in period t = 0 FV0

is equal to the sum of discounted future cash flows (DCF model)

(1)

where EBITt denotes EBIT in period t It are investments in new capital in period t WACC is the

weighted average of cost of capital and denotes the cash tax rate Note that WACC and do not

have a time subscriptmdashthat is they are constant This assumption is not too restrictive and is

FV0

=EBIT

1acute 1-t( ) - I

1

1+WACC( )+

EBIT2acute 1-t( ) - I

2

1+WACC( )2

+EBIT

3acute 1-t( ) - I

3

1+WACC( )3

+


frequently applied in practice because these metrics only change as a result of substantial

exogenous shocks (eg a recession a change in tax law) which are difficult to predict

Copeland et al (2013 497ff) show that Equation 1 can be simplified and rearranged such

that it decomposes firm value into two parts (see Web Appendix W1)

(2)

The first part of Equation 2 collects future cash flows generated from the capital invested at the

time of valuation It reflects the value of current earnings strength Although value generation is

associated with the current earnings level most of this value is generated in the future through

earnings persistence The second summand measures the value of growth expectations It shows

that the value added is a mix of growth and return expectations which are reflected in the four

key value drivers ROIC EGR S and WACC The expression demonstrates several fundamental

implications for value creation First value creation without growth in earnings is not possible

Second growth per se is not the objective and can even destroy value when ROIC lt WACC

Only profitable growth is valuable which requires ROIC gt WACC Finally the expression

reveals that S plays an important role as it limits value creation from profitable growth This is

because no competitor can expand and earn more than the cost of capital on the investment in a

long-term competitive equilibrium (Demsetz 1982) Only firms with a significant competitive

advantage can sustain excess return over a longer time (Dierickx and Cool 1989)


Theoretical Framework of Value Drivers

Equation 2 is our core valuation equation Figure 2 summarizes the theoretical framework

that guides our empirical analysis Consistent with prior research (eg Stahl et al 2012) we

propose that marketing actions such as advertising investments contribute to brand building

which we measure in terms of CBBE We propose that CBBE potentially influences firm value

via its value drivers Figure 2 shows the endogenous variables and the associated estimation

equation Note that for completeness and to account for potential endogeneity of our key CBBE

construct we estimate five equations however our substantive interest focuses only on the three

value drivers ROIC EGR and S

Mechanisms of Value Generation by Brands

Marketing theory has produced a rich literature that explains how brands create value for

customers (eg Fischer et al 2010 Keller 1993) and companies (eg Aaker 1991 Kapferer

2011 Srivastava et al 1998) Aaker (1991 16) suggests six mechanisms through which brands

may add value for the firm (for a similar set see Keller 2008 88ndash91) Srivastava et al (1998 6)

also refer to these mechanisms but discuss them with a broader scope of how intangible market-

based assets enhance value Next we review each of these value creation mechanisms and how

they map onto the three focal value drivers (see Table 2) In our discussion we focus on the most

effective relation between a mechanism and a value driver By no means does our focus on the

main relation imply that the mechanism has no relevance for other value drivers rather shedding

light on the mechanisms and their main relation to value drivers can help managers prioritize

actions and derive a focused value-based brand exploitation strategy

Prices and margins Powerful brands are characterized by high awareness high perceived

quality (relative to its price positioning) and strong and differentiated brand associations (Keller

1993) These features can help consumers retrieve information and offer a reason to buy (Aaker


1996 p 9) They often translate into monopolistic power for brands that enables them to

command a significant price premium (eg Ailawadi et al 2003) As a result a high-equity

brand does not lose market share (up to a point) when its price increases or competitors lower

their prices This is the core idea behind brand valuation methods based on their price premium

(eg Swait et al 1993)

The aforementioned features of powerful brands may also result into a major advantage for

the brand in choice situations Awareness and faster retrieval of information increase the

likelihood that the brand enters the customerrsquos consideration set which is a necessary condition

for choice (Nedundagi 1990) Strong brand (quality) associations significantly contribute to

creating brand preference and this advantage translates into higher market share (volume

premium) The preferencendashmarket share link is the key mechanism in brand valuation models

that focus on the volume premium of brands (eg Park and Srinivasan 1994) Not only does the

market share advantage have a revenue effect but it may also improve costs For example higher

volume enables the firm to benefit from economies of scale and scope (Spence 1980) marginal

cost can decrease from riding down the learning curve and margins can improve due to increased

market power which can be leveraged to reduce purchase cost

The brand mechanism outlined herein yields higher prices andor lower costs It therefore

primarily drives the return on invested capital (see Table 2)

Efficiency and effectiveness of marketing programs Keller (1993 8) defines CBBE ldquoas the

differential effect of brand knowledge on consumer response to the marketing of the brandrdquo This

understanding implies that existing and prospective customers are more responsive to marketing

activities by a high-equity brand For example higher awareness and positive favorable and

strong brand associations can improve the effectiveness of a new product promotion activity in

the sense that potential buyers will be less skeptical of brand quality (Aaker 1991 p 16f) A


positive brand image also motivates customers to invest more resources into information search

which can include the willingness to seek out new distribution channels (Keller 1993) Customers

are less responsive to price increases as noted previously Finally we have ample evidence that

advertising response and carryover patterns are much stronger for high-equity brands than for

weaker brands Consumers with salient brand and product associations require fewer ad contacts

and thus fewer resources to achieve communication objectives (Percy and Elliott 2009) All these

brand effects enhance the profit margin which is why this brand mechanism is predominantly

associated with the value driver ROIC

Brand loyalty Researchers have long argued that strong brands provide reasons to buy and

increase customer satisfaction (eg Stahl et al 2012) As a result the customer base becomes

more loyal which entails several value-enhancing effects A loyal customer is less likely to

switch to competitive offerings Consequently cash flows generated from these customers are

less vulnerable to future competitive attacks (Srivastava et al 1998) Indeed a strong brand-loyal

customer base may erect significant entry barriers that keep potential new entrants out of the

market (Aaker 1991 47) As a result firms with such a customer base face less competitive

pressure which can drive down excess return Therefore we conclude that the brand loyalty

value mechanism is primarily associated with enhancing the sustainability of excess return

Satisfied and loyal buyers are also more responsive to marketing efforts which reduces the

costs of marketing programs Loyal customers show higher willingness to pay and therefore

accept higher prices (Srivastava et al 1998) Finally retaining a loyal customer requires much

less investment than acquiring new prospects (eg Aaker 1991 47 see also Stahl et al 2012)

We conclude from these effects that brand loyalty also influences the value driver ROIC

Brand extensions Strong brands offer a greater potential to extend existing product lines

expand into related and new product categories enter international markets and increase


revenues by licensing brand names to be used in other categories (eg Lane and Jacobson 1995

Srivastava et al 1998) It is the greater awareness and positive associations potential customers

hold with respect to a strong brand that reduce entry barriers and result into faster trial referrals

and adoption and stronger preferences for the new product (Keller 1993) Aaker (1996 292f)

differentiates between brand extensions and range brands Brand extensions are incremental and

short-term oriented They are driven by searching for product classes that fit with the brand and

allow the quick introduction of a new product The core benefit arises from the lower risk profile

associated with the extension Developing a range brand however is strategic and builds on the

creation of a brand asset with a real competitive advantage across product categories This

strategy may involve altering the future identity of the brand An example of a successful range

brand is Kraft with its lines Philadelphia Kraft slices and Kraft Mayonnaise (Aaker 1996 295)

Another powerful driver of revenue growth is brand alliances that include ingredient

branding Among the most successful ingredient branding strategies is the ldquoIntel Insiderdquo

campaign Because leading computer manufacturers put the campaign logo on their products

customers developed trust and strong quality perceptions of Intel They arguably inferred these

positive associations from the original equipment manufacturersrsquo decision to inform about the

Intel processor inside Intelrsquos worldwide sales increased by 63 in the first year of the campaign

(Aaker 1996 p 12) Considering these powerful growth effects we associate the brand extension

mechanism primarily with earnings growth

Trade leverage Trade leverage is another value-creation mechanism that is linked with

growth It refers to the observation that strong brands enjoy significant advantages in retail

distribution Like consumers distribution partners associate less risk with new product launches

when they come under the umbrella of an established brand name A brand with a loyal customer

base is also more attractive to retailers because it promises stable revenues from selling the brand


As a result strong brands are more successful in the competition for scarce shelf space Finally

cooperating with high-equity brands in promotion activities also suggests higher effectiveness for

retailers (Aaker 1991 18) Trade leverage is most likely to turn into revenue growth which is

why we connect this mechanism primarily with the value driver EGR

Competitive advantage Strong brands are a major source of competitive advantage that can

result in excess return though this excess return can only be realized over a longer period if the

firm is able to maintain the competitive advantage The resource-based view of the firm posits

that a firm reaches a sustainable competitive advantage by virtue of unique resources that are

rare valuable inimitable and nonsubstitutable as well as firm-specific (eg Makadok 2001)

The brand offers such a resource that protects the company from competition and makes its future

cash flows less vulnerable (Aaker 1991 Srivastava et al 1998) Consumers have greater

preferences for strong brands These preferences impose switching costs that result in greater

loyalty (Chaudhuri and Holbrook 2001 Keller 1993) Dominant brands often act as a reference in

their category (Coca Cola Airbnb etc) and increase barriers for the entry of new competitors

(Kapferer 2011 24 Spence 1980) This unique position also shields the brand against

competitive actions along the marketing mix (Mela et al 1997) All these effects suggest lower

pressure from incumbents and new entrants on the firmrsquos own margin over time Thus

competitive advantage represents a mechanism that is primarily linked with the sustainability of

excess return

Econometric Model Specification

Modeling Requirements

Our empirical design includes model equations for our focal value drivers ROIC EGR and

S In addition we model CBBE and WACC to account for their endogeneity when using these

variables in other equationsmetrics Before we turn to the specifications we briefly discuss


several requirements our equations must satisfy Specifically we need to model expectations and

account for heterogeneity dynamics diminishing returns and the influence of control variables

Expectations Market valuation of a business is based on investors expectations about the

stream of future cash flows Thus our focal value driver variables are expectations about ROIC

earnings growth WACC and sustainability of excess return Ideally we would ask investors for

their expectations For earnings growth we have such information from a regular survey among

analysts available We derive the expected sustainability of excess return from firm market

values We adopt a modeling approach to measure expectations for ROIC and WACC

Heterogeneity We pool data from various firms and markets for model estimation We thus

need to control for idiosyncratic differences in our focal constructs that arise from firm and

market differences We include firm size and market concentration as two observable

heterogeneity variables In addition we specify the intercept in each equation as firm-specific and

assume that these effects follow a random distribution (as in eg Fischer and Himme 2017) By

incorporating firm-specific effects we also effectively control for omitted firm characteristics

such as management luck or other market-based assets which we cannot observe Because we

model the unobserved firm characteristics in a Bayesian fashion as part of the intercept they do

not appear in the error term We thus circumvent endogeneity issues that may arise when other

predictors correlate with unobserved firm characteristics as part of the error term Finally we

specify the parameters for advertising in the brand equity equation and for CBBE in all other

equations to be heterogeneous which enables us to measure firm differences in their

effectiveness of influencing CBBE the value drivers and ultimately firm value

Dynamics We include the lagged dependent variable to control for carryover effects This

specification corresponds to the established and parsimonious notion of geometrically distributed

lags (Hanssens et al 2001) Another advantage is that the impact of other predictors can be


interpreted more readily as (Granger) causal It also controls for different initial conditions (Tuli

and Bharadwaj 2009) We check for other dynamics such as nonstationary time series and serially

correlated error terms but find no evidence for these characteristics2

Diminishing returns Marketing investments should be subject to diminishing returns which

is also a necessary condition for the existence of an optimal investment level (Hanssens et al

2001) To account for this factor we take the log of advertising and other expenditure variables

in the brand equity equation which can be interpreted as our marketing productivity equation

Control variables We include various control variables that are assumed to influence our

focal constructs selecting them in line with prior research in finance accounting marketing and

strategy These controls cover strategic variables (eg RampD expenditures) financial variables

(eg financial leverage) and variables of operational efficiency (eg operating margin) We also

account for economy-wide period-specific influences by incorporating the growth in US gross

domestic product (GDP) Because we focus on the effects of CBBE on the value drivers we do

not discuss the control variables in detail Web Appendix W4 lists the various control variables

assigns the equation where they appear and provides references from supporting literature

2 The test for common factors (Greene 2012) does not suggest serially correlated errors (one- and two-period lagged

p gt 10) Using panel unit-root tests (Fisher-type based on augmented Dickey-Fuller tests Choi 2001) we cannot

reject the null hypothesis of nonstationary time series


Specification of Estimation Equations

CBBE equation For measuring the impact of advertising investments and other variables on

CBBE we specify the following equation

(3)

with

where a denotes the vector of parameters to be estimated i is an index for firm t is an index for

period and u is an iid error term Appendix Table A2 summarizes the symbols and

abbreviations we use for predictor variables in Equation 3 and the following equations Vector ai

includes the parameters that are assumed to be firm specific where is the mean and wai is a

random vector with mean zero and variance matrix equal to an identity matrix We allow the

firm-specific parameters a0i and a2i to be correlated Matrix provides the covariances and

variances of the assumed multivariate normal distribution of ai We impose the same flexible

structure on the parameter vectors in all other equations We measure carryover by the parameter

a1 The use of lagged values for the predictor variables avoids potential endogeneity issues

Profitability equation Let measure the expected return on invested capital We

assume that investors form their expectations on the basis of the following information set

(41)

with

where b denotes the parameter vector to be estimated and all other terms are as defined

previously Note that investors can only use past information to build expectations about future

CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb


ROIC In period t expected then explains realized ROICit up to an error which we

denote with and assume to be iid normal distributed Thus

(42)

Inserting Equation 41 into 42 then produces an estimation equation that includes only observable

quantities which we take to the data

Earnings-growth equation We specify expected earnings growth as follows

(5)

with

where c denotes the parameter vector to be estimated and all other terms are as defined

previously Earnings growth expectations are available to us from a regular survey among

analysts Because the mean is subject to sampling error that depends on the number of analysts it

introduces heteroskedasticity into the error variance We account for this by using the number of

analysts as a weight when estimating the model

Cost-of-capital equation Building on previous research in the marketingndashfinance interface

(eg Fischer and Himme 2017 Rego et al 2009) we specify the following equation to predict

expected cost of capital

(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd


where d denotes the parameter vector to be estimated and all other terms are as defined

previously Expected explains realized WACCit in t up to an error which we denote

with and assume to be iid normal distributed Thus

(62)

Inserting Equation 61 into 62 then produces our estimation equation

Sustainability-of-excess-return equation We now turn to our last estimation equation to

explain expected sustainability of excess returns Recall that this variable measures the length of

the period during which the firm is expected to earn rents above its cost of capital As a result

is a duration variable that is nonnegative by definition This requires an appropriate distributional

assumption and estimation approach such as a hazard model (Greene 2012) The Weibull

distribution is a very flexible distribution that allows for both monotonic and nonmonotonic

shapes of the marginal distribution and encompasses the exponential distribution as a special case

(Greene 2012) We adopt this distribution and test whether this assumption is supported by our

data (see the Web Appendix Figure W6)

Note that because the duration of superior rents is a unique event that follows a random

distribution it is conceptually not apt to include the lagged dependent variable into the model

We specify our last equation for expected sustainability of excess return as follows

(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg


where describes the density of expected sustainability is the location parameter and p

is the scale parameter that characterizes the moments of the distribution and are to be estimated g

denotes the parameter vector to be estimated and all other terms are as defined previously

Again we consider one-period-lagged values for all predictor variables to account for the fact

that investors use prior information levels when forming their expectations We adopt a hazard

function approach to estimate the parameters in Equation 7 (Greene 2012)

Data and Estimation

Data Sources

We collected data on an annual basis from various databases including Harris Poll

EquiTrend COMPUSTAT Bloombergrsquos the Center for Research in Security Prices (CRSP) and

IBES Our data collection covers the period 2005ndash2013 The sample includes 613 companies

from major industry sectors such as telecommunication services consumer packaged goods and

so on The total number of observations exceeds 5000 However because we do not necessarily

observe all variables for each firm and period the effective sample size is considerably smaller

and varies by equation Our panel is considered unbalanced because a very small portion of firms

enters or leaves the panel due to firm birth death or merger and acquisition events

We must acknowledge that the EquiTrend database is biased toward larger brands that are

owned by larger firms and this is also a feature of our estimation sample because larger firms

tend to offer more complete financial data However the samples do not differ in terms of

financial variables such as liquidity leverage and operating margin (see Web Appendix W4 and

Table W41) Strictly speaking our results do not generalize to small firms with weak brands


Measures We use the established EquiTrend data to measure CBBE (eg Bharadwaj et al

2011) The measure is a latent variable scaled to a 0ndash100 index and estimated using four

individual-level consumer variables familiarity perceived quality purchase consideration and

distinctiveness (see Web Appendix W3 for a detailed description) Following prior practice

(Rego et al 2009) we aggregate mean ratings of different brands for multibrand firms though the

majority in our sample are mono-brand firms (80) By ldquomono-brand firmrdquo we mean that the

firm owns one dominating brand that accounts for all or the lionrsquos share of the business

We use COMPUSTAT data to construct our ROIC variable (see Appendix Table A3 for

details) Our expected earnings growth variable represents the five-year consensus forecast of

analysts provided by the IBES database Investors broadly rely on these analyst forecasts

(Kothari 2001) Bloomberg provides all information we need to calculate WACC We follow the

standard approach (eg Rego et al 2009) and estimate firm-specific beta on a yearly basis by

using daily stock returns for each firm Together with information on credit spreads the yield of a

risk-free bond and the capital structure we obtain WACC for each year and firm

Sustainability of excess return is a latent construct and not observable From our DCF model

however we know that it is an inherent part of the valuation process Assuming efficient capital

markets it is implicitly incorporated in a firmrsquos current market value Consequently we solve

Equation 2 for (for details see Web Appendix W1)

(8)


where firm value FVit is the sum of the average market value of equity over trading days of a year

and the book value of debt (COMPUSTATs DATA 9) as of December 31 of the respective year

Equations 4 and 6 provide values for and is based on the five-year

consensus forecasts by analysts and CRSP and COMPUSTAT deliver the remaining

information

Control variables We obtained financial data such as leverage dividend payouts size and

so on as well as data on marketing and RampD expenditures from COMPUSTAT We compute

the C4-concentration index by aggregating the market shares of the four largest firms at the two-

digit North American Industry Classification System (NAICS) level Appendix Table A3

provides further details on the definition and data sources of variables used in our analysis Note

that we do not model stock returns but rather absolute firm value as a result of a corporate

valuation model For that reason we are not concerned with different release periods as all

information is properly aligned at year-end

Descriptive Statistics and Model-Free Insights

Table 3 presents the descriptive statistics for our data Mean CBBE is 5629 Mean return on

invested capital corresponds to 22 Analysts forecast the five-year earnings growth to be 13 on

average The mean cost of capital amounts to 09 during the period 2005ndash2013 Investors expect

that the average firm in our sample has a sustainability period of 14 years during which the firm

may enjoy profitable growth (ROIC gt WACC) This finding is in line with Rappaport and

Mauboussinrsquos (2001) conclusion that a period of at least 10 years is required for most listed

companies to justify their market valuation Note we estimate a period of 0 years for 74 of 491

cases (15) which reduces the median to 84 years Appendix Table A1 shows the correlation

matrix for our model variables We observe no excessive correlation suggesting collinearity


issues which is also supported by variance inflation factors (VIF) The largest VIF amounts to

379 far below the threshold of 10 (Greene 2012)

We conduct simple mean-difference tests to generate first insights from a model-free

analysis Table 4 summarizes the results Here we build two groups that include observations

with low CBBE versus high CBBE We then compare the group means for our key performance

variables Panel A shows the results if we split the sample according to the median CBBE In

Panel B we compare the means between the lowest and highest quartiles in terms of CBBE

Results of the difference test provide first evidence in favor of our expected brand-leverage

effects We observe significant differences in terms of firm value ROIC earnings growth

forecasts and the sustainability of excess return Firms with stronger brands enjoy higher firm

values profits and earnings growth as well as a longer period of excess return Unsurprisingly

these differences are more pronounced when we compare the end quartiles of the distribution of

CBBE However we find no significant differences in the cost of capital

Estimation

Estimation approach We use a two-step simulated maximum likelihood approach with

instrumental variables (IVs) for estimation (eg Fischer and Himme 2017) Under the usual

regularity conditions this estimator is consistent and asymptotically normal distributed We use

instrumental variables in Equations 4ndash7 to reduce the danger of biased estimates that may result

from a potential simultaneity between CBEE and the drivers of firm value It is possible that

firms anticipate investorsrsquo expectations for ROIC for example which in turn influences their

current investments in CBBE to meet these expectations

Identification The large variation of our focal variables across and within firms provides the

source for identifying effects However we need to account for potential simultaneity issues with

respect to CBBE and employ IV estimation which requires that we have sufficient and


appropriate instruments available to identify CBBE Except for CBBE and the lagged dependent

variable we treat all other predictors in an equation as predetermined variables which we test

for and thus as potential instruments To properly identify CBBE we must use information

outside the equation which Equation 3 provides Here we assume that CBBE in year t results

from prior investments in advertising RampD and other activities Brand investments are also

likely to be higher when previous yearrsquos earnings and operating margin are higher Because

CBBE enters the value driver Equations 4ndash7 with a lag of one year we use two-years-lagged

values of the predictors of Equation 3 (excluding lagged CBBE) as instruments (for details see

the Web Appendix Table W5) Web Appendix W5 describes results of common tests to check

for the validity and strength of our instruments which provide strong evidence thereof

Finally we note that estimation of the carryover coefficient associated with the lagged

dependent variables in Equations 3 to 7 may cause identification problems (Arellano 2003) The

lagged dependent does not only accommodate dynamic effects but also tends to pick up firm

heterogeneity Following Fischer and Himme (2017) we instrument the lagged values with their

deviations from the firm-specific mean to isolate the true carryover effect The well-known

endogeneity issue associated with the lagged dependent variable in a (differenced) fixed effects

model does not arise here We do not take first differences and do not specify firm-specific

effects as part of the error term Instead of that we model a random intercept


Empirical Results

Table 5 summarizes the estimations results for Equations 3ndash6 on CBBE expected ROIC

expected earnings growth and expected WACC Table 6 shows the results for the sustainability

of excess return Equation 7 which we estimate using a hazard model approach Pseudo-R2 ranges

from 65 (WACC equation) to 91 (CBBE equation) We consider an R2 gt 50 to be meaningful

for explaining variance in a large panel data set In addition we check whether our corporate

valuation model (model 2) is a good approximation of observed market values of firms For this

purpose we insert predicted values from Equations 4ndash7 into Equation 2 to predict firm values

The correlation with observed market values amounts to 9543 in other words our model is able

to explain more than 90 of the variance of observed market values

All equations reveal strong heterogeneity of firms as reflected by the significant standard

deviation of the intercept term which suggests indeed important firm-specific factors such as

management quality or other market-based assets In addition we find moderate to strong

carryover effects across Equations 3ndash6 All significant coefficients of the control variables show

the expected direction and are in line with prior empirical studies For the sake of brevity we do

not discuss these results in detail but focus on the effects associated with CBBE

Parameter Estimates Related to CBBE

We observe that advertising expenditures drive CBEE ( p lt 01) which is

consistent with previous findings (eg Stahl et al 2012) and that CBEE itself has the expected

positive influence on ROIC ( p lt 05) Our estimation results also show that CBBE

drives earnings growth ( p lt 01) The average impact of CBBE on WACC is positive

3 Note that values obtained with Equation 2 are subject to measurement error which dilutes the correlation estimate

By using the estimations errors of Equations 4ndash7 and the delta method we approximate the measurement error for

Equation 2 The corrected correlation increases to 99 but also involves a wider confidence interval

a

2= 413

b

2= 001

c

2= 003


but not significant ( p gt 10) Finally we find strong evidence for the role of CBBE in

establishing a competitive advantage that ensures a longer period of excess return for the firm

( p lt 01 see Table 6) The estimated standard deviations of the CBBE parameters are

also significant (p lt 01) which suggests substantial variation in the brand-leverage effects across

firms

Elasticity Estimates for Value Drivers and Firm Value

An important objective of our study is to understand and compare the relative importance of

CBBE in influencing value drivers and how value drivers influence firm value factors that are

not easily intuited Because parameter estimates are not directly comparable we transform them

into elasticities Specifically we use the conditional estimates of firm-specific parameters (which

correspond to the posterior mean in a Bayesian setting) together with firm-specific means for

CBBE value drivers and market value of the firm to compute these elasticities Web Appendix

W2 provides details on how we calculate each of the elasticities We report total effects and

account for carryover in the calculation of elasticities for ROIC and EGR with respect to CBBE

Specifically we divide the estimated CBBE parameter by (1 ndash carryover)

Table 7 shows elasticity estimates across 13 industries which are based on the Global

Industry Classification Standard Because the number of firms included in the industry group is

usually below 30 we do not report means and associated significance statistics which are biased

in small samples We report the median which is less influenced by outliers than the mean and

therefore draws a more realistic picture of the situation in the industry The last row shows

overall values across industries Here we report the mean with its p-level and the standard

deviation

d

2= 1109

g

1= 011


CBBE effects on value drivers Table 7 demonstrates that CBBE has on average a

substantial influence on all three value drivers The largest mean elasticities are associated with

earnings growth (=178 p lt 01) and sustainability of excess return (=122 p lt 01) The effect is

smaller for profitability (=172 p lt 01)4 Note the elasticity depends on not only firm-specific

parameters for CBBE but also the level of CBBE and the level of the value driver

Considering results by industry we observe a strong variation for each of the value-driver

elasticities The median leverage effect for CBBE on profitability is three to four times higher for

health care and telecommunication services (342 and 339) than for consumer services and media

(076 and 116) The differences are even larger for the leverage effect on earnings growth The

industry median ranges from ndash211 for automobiles and components to 900 for

telecommunication services Note these elasticities cannot be interpreted as sales elasticities

because the dependent variable earnings (growth) is a profit measure A negative elasticity

occurs if a firm is overinvested in an intermediate performance variable such as CBBE (Edeling

and Fischer 2016) The large variation in EGR elasticities across industries is also reflected in its

standard deviation of 654

The last two columns of Table 7 present results on firm-value elasticity with respect to

ROIC EGR and S Note that by construction the elasticity is the same with respect to earnings

growth and sustainability of excess return The overall means across industries are practically the

same with 390 for ROIC and 372 for EGR and S However we find again a large variation for

all elasticity measures across industries The median impact of ROIC on firm value ranges

from 079 for health care to 575 for media The median impact with respect to EGR and S may

be as low as 020 for telecom services and as high as 586 for transportation Firm-value

4 For comparison the elasticity of ROIC for RampD expenditures amounts to ndash016 and for EGR to 324 It is not

significant for S


elasticities with respect to EGR and S are higher compared to ROIC in most industries The

reverse however is true for media information technology and industrial energy and utilities

We do not observe much difference in transportation Interestingly we find ROIC to be the

strongest driver of firm value in the media industry with an above-average elasticity of 575

paired with a below-average leverage effect of CBBE on ROIC of 116 This finding suggests

that brand exploitation strategies of firms in this industry are not well aligned with the relevance

of this value driver for value creation We delve into this issue in more detail in the ldquoDiscussionrdquo

section

Robustness Checks

We performed several additional analyses and robustness checks Specifically we tested how

sensitive our results and conclusions are with respect to the distributional assumptions of the

omission of other market-based assets such as customer strength the specification of dynamics in

the models the use of alternative estimation approaches the stability of the CBBE parameter

over time and the composition of the sample For the sake of brevity we do not report on these

robustness checks here but refer to the Web Appendix for full details (section W6 with Figure

W6 and Tables W61ndashW68) We obtain consistency with and support for our focal model results

Thus we conclude that our results are not driven by model assumptions omission of important

variables model specifications the selection of estimation approaches or the composition of the

sample

Using the Results to Analyze the Strategic Situation

Firm value is strongly driven by investorsrsquo expectations about the rate of return on new

invested capital the future growth in earnings and the length of the period during which a firm

can earn excess return We focus herein on the role of CBBE in driving firm value via these three


value drivers Table 7 provides answers on the relative impact of CBBE on value drivers and on

the importance of the value drivers for firm value We find substantial across-firm variation of

both CBBE leverage effects and relative importance of value drivers for value creation These

findings suggest that a detailed analysis of each firmrsquos situation is necessary to determine the

most effective brand exploitation strategy

Evaluating the Brand Exploitation Strategy

In view of our findings brand managers and senior executives are well advised to start with a

thorough situation analysis before they decide about investments in their brand exploitation

strategy Value-based brand exploitation means effectively leveraging a brandrsquos potential for

improving profitability andor growth as the two major sources of firm value creation While

theoretically possible most firms probably will find it difficult to achieve higher profitability and

growth at the same time as it may overstretch the brand potential and exceed available resources

Therefore management needs a strategic focus in its brand exploitation strategy To achieve this

focus a simple evaluation matrix can help (see Figure 3)

The matrix combines two dimensions for analyzing the brandrsquos current value creation

options with respect to the three value drivers The X-axis identifies the importance of value

drivers for value creation relative to one another We measure this importance by the firm-value

elasticity with respect to the value driver The Y-axis shows the extent to which the brand

leverages its customer-based equity to enhance the specific value driver The leverage effect is

measured by the value-driver elasticity with respect to CBBE and compared with its industry

average5

5 We acknowledge that assignments to high and low categories might be too strict in cases in which leverage effects

are close to the industry average or value driver effects on firm value are about the same size One option would be

to introduce a medium category The resulting 3 times 3 matrix however considerably increases the complexity of

decision making


All else being equal two factors drive these elasticities up or down First both types of

elasticity metrics decrease in their respective value driver which reflects the saturation effect For

example the higher the achieved ROIC level the lower its potential to drive firm value and to be

driven itself by higher levels of CBBE Second marginal effects drive both elasticity types A

value driverrsquos marginal effect on firm value depends on the level of other value drivers and

financial metrics such as EBIT and the tax rate The estimated coefficient associated with CBBE

in Equations 4ndash7 is the key determinant of the marginal effect of CBBE on the focal value driver

These marginal effects vary substantially across firms and reflect firm-specific capabilities of

exploiting its brand for enhancing the value driver Because capabilities are likely to be limited

by industry conditions we put the firm-specific value-driver elasticity with respect to CBBE in

relation to the industry average

It is now straightforward to draw conclusions from positioning the value drivers in the

matrix The analysis may lead to three possible outcomes First assume a value driver has low

(high) importance for growing firm value and the associated CBBE leverage effect is also low

(high) Then the brand potential is appropriately leveraged Second the leverage effect of CBBE

is high but the value driverrsquos relative importance for value creation is low In this case the firm is

wasting its resources Third we may have a value driver that is highly important for growing firm

value but its brand leverage effect is below average Here the firm has failed to exploit the

leverage potential of its brand to its full extent The first outcome suggests the firm is on track

with its brand exploitation strategy Management should focus its resources on becoming more

efficient in what the firm is doing The major conclusion from the second and third outcomes in

contrast is to refocus its current brand exploitation strategy and reallocate resources if industry

and firm-specific conditions allow for doing so Limitations may result from competitive


strategy path dependencies a firmrsquos own resources and capabilities and so on which we do not

consider here

CVS Health and Kroger Revisited

We started this article with an example of two US retailers the pharmacy CVS and the

grocery retailer Kroger In our observation period both companies were of similar size and their

brands showed a similar CBBE rating However the difference in market capitalization and

estimated financial brand values were considerable Using the strategic analysis tool (Figure 3)

together with the estimated brand-leverage and value-driver effects we are now in a position to

evaluate the brand exploitation strategy of the two firms For both firms EGR and S are more

powerful for value creation than ROIC Estimated elasticities are 377 (ROIC) versus 542 (EGR

and S) for CVS and 076 (ROIC) versus 283 (EGR and S) for Kroger The estimated value-driver

elasticities with respect to CBBE are as follows 156 (ROIC) 499 (EGR) and 191 (S) for CVS

and 338 (ROIC) 555 (EGR) and 202 (S) for Kroger Table 7 shows the values for the average

brand-leverage effects in the food amp staples retailing industry

Figure 4 shows the results of our ex post brand exploitation strategy analysis which confirms

that the CVS management followed a well-aligned brand exploitation strategy A key focus of its

strategy was to redesign stores and extend the range of products for fitness nutrition and beauty

that were most relevant to women their largest customer group (Keller 2008 320f) Kroger in

contrast adequately leveraged its brand potential only with respect to the sustainability of excess

return The brand-leverage effect of CBBE on ROIC exceeds the industry average but

significantly underperforms with respect to EGR As a result resources appear to have been

wasted on leveraging the brand to drive profit margins whereas the potential to drive growth

remained untapped We caution that our conclusions from the ex post analysis do not draw a

complete picture of the situation Other explanations are possible for the market cap differences


such as differences in the business model or financial health of the firms Our story however

offers at least a complementary explanation that is based on strong empirical insights

Implications for the Theory and Practice of Brand Management

Extensions to Brand Literature

Our study results have important implications for brand management theory and practice

which we discuss subsequently Prior research has produced ample evidence on the relevance of

brand investments for value creation (see Table 1) We extend this literature by modeling the

mechanisms of value generation derived from the standard DCF model of corporate valuation

Our analysis is important because it opens the black box and sheds light on how a brand can

actually leverage its potential for value creation Most brand experts in science and practice

probably agree that brands improve each of the value drivers to a greater or lesser extent

However the relative magnitude of these effects is not well understood

Our study is also an important step toward a better understanding of how brands appropriate

value in the marketplace Compared with the rich literature on building brands (eg Keller and

Lehmann 2006) research on how to leverage brands is limited A notable exception is Datta et al

(2017) who investigate the translation of CBBE dimensions into product market outcome

measured by the revenue premium approach (Ailawadi et al 2003) The lack of further studies

however is surprising in view of Mizik and Jacobsonrsquos (2003) finding that the stock market

rewards the strategic emphasis on value appropriation more than it rewards value creation We

demonstrate how firms may appropriate value in the financial market from one of their most

valuable assets Our study thus complements Datta et al (2017) by focusing on value

appropriation in the financial market


Conceptually we also advance the brand management literature We argue that it is not

sufficient to only focus on brand building activities to drive CBBE It is at least equally important

to develop a value-based brand exploitation strategy As a first step we need to understand which

value driver is most relevant for the firm Our strategic analysis tool (Figure 3) may help setting

the right focus for brand exploitation In the next step management must execute the brand

exploitation strategy Our previous theory discussion shows there are different mechanisms by

which brands translate their equity into value via the three value drivers (see Table 2)

Understanding these mechanisms can help a firm derive a plan for strategically using the

marketing mix

Marketing Mix Impact on Value Drivers

Managers may find Table 8 useful as a guide in their decision making If the objective is to

improve profitability it is most likely to be achieved by establishing price premiums in the

market and becoming more efficient and cost-effective in the execution of marketing programs

which warrants more emphasis on price and contracting as well as communication and

advertising activities In contrast if the exploitation focus is on (earnings) growth the brand

potential should rather be used to extend product lines increase penetration in existing markets

or form powerful brand alliances which suggests an emphasis on product and innovation

activities and on distribution and retail strategy Finally we believe that innovative new products

and the powerful communication of a unique brand position are especially promising in building

the competitive advantage to maintain excess return over a longer period Thus resources should

be allocated to product and communications activities if the sustainability of excess return is the

focus of the value-based brand exploitation strategy We caution that our discussion does not

suggest a mutually exclusive use of marketing mix elements for enhancing a specific value

driver For example new products may well contribute to higher profitability The purpose of


Table 8 is to provide guidance about mix elements that are on average deemed to be most

powerful in influencing the focal value driver

Effective Brand Exploitation Strategies and Programs

Managers may also ask which brand exploitation strategies (eg brand positioning global

branding co-branding ingredient branding brand extension licensing internal branding cause-

related marketing employer branding see eg Aaker 1991 Keller 2008) in general can

influence a given value driver most effectively While each of these strategic options include

elements of both brand building and brand exploitation one element is likely to dominate the

other depending on the strategy We believe that brand positioning global branding ingredient

branding internal branding and cause-related marketing first and foremost require investments to

build the brandmdashthat is establish a strong customer-based brand equity as reflected in

Equitrendrsquos brand rating measure In contrast co-branding brand extension licensing and

employer branding offer ways to leverage a strong CBBE to appropriate value What are

examples of effective brand exploitations strategies and programs In the following subsections

we discuss a few examples along the value drivers although this discussion should not be

considered exhaustive

Focus on profitability (ROIC) A focus on ROIC implies that the firm tries to leverage the

strength of its brand for increasing prices lowering costs of service or utilizing the potential for

price discrimination Raising prices is always a dangerous venture that can easily backfire

because customers may perceive it as unfair It must be accompanied by a meaningful reason A

sustainable strategy to exploit a brandrsquos potential for price increases is to systematically plan and

connect future product generations with price increases Car manufacturers such as Mercedes-

Benz and BMW have been following this strategy with great success Another example is Apple


Its iPhone 3GS with 32 GB memory was introduced at $299 in 2009 The price rose step by step

with each new generation Apple sold its iPhone 7 32 GB for $649 in 2016 an increase of 117

in seven years and the recently launched iPhone X with 64 GB starts at $999 Of course new

features have improved the iPhone over time but prices for memory and central processing units

have declined The combination of a strong brand with a strategic plan for successive product

generations broadens the scope for price increases that customers will find acceptable in a

competitive market

The value-oriented analysis of customer data from a loyalty program may also offer ways to

improve ROIC using the strength of the brand With the rise of low-cost carriers and state-

subsidized new rivals from the Middle East more than a decade ago established premium airlines

such as Lufthansa came under enormous pressure Staying profitable became the priority

challenge to satisfy investorsrsquo expectations Competing on prices was not an option given

Lufthansarsquos weak cost position but exploiting the strength of its brand was Management started

a program to leverage the considerable amount of available data to better understand how its gold

status customers called senators prefer to interact with the brand While some customers were

proud to be a senator and preferred to be recognized as such in public others preferred less public

presence but valued the personal address by representatives of the company Using these

findings Lufthansa developed customer-specific approaches to interact with the brand and

defended its price premium in this important customer group against the new competitors

Focus on earnings growth (EGR) The brand may also be a powerful asset to leverage for

growth Such growth could come from extending the brand into new categories and markets

There are plenty of examples of successful brand extensions (eg Colgate Virgin) although the

risks of this strategy should not be overlooked (Keller 2008 502ndash511) Licensing is another


option that involves less risk of losing money as there are no manufacturing or marketing costs

involved Revenues generated by the licensor translate directly to profits

The strength of a (corporate) brandrsquos reputation can also help attract partners to form brand

alliances When Boehringer Ingelheim (BI) a medium-sized family-owned research-based

pharmaceutical company was about to launch its innovative chronic obstructive pulmonary

disease drug Spiriva in 2002 it had closed a deal with Pfizer to co-market the new drug Given an

already high profit margin the key challenge was to unlock the growth potential BIrsquos excellent

corporate image and reputation among pulmonologists helped attract the big partner and opened

up a growth potential BI would not have been able to exploit alone Pfizer significantly

broadened market access and brought in its specific expertise to develop an indication (Van den

Bergh and Gerlof 2012) By 2011 Spiriva achieved a blockbuster sales level of euro315 billion

Focus on sustainability of excess return (S) Maintaining a competitive advantage as

reflected in excess return over the long run is probably the toughest challenge for marketers A

prerequisite is a brand-loyal customer base that is willing to pay premiums despite the rise of

lower-priced competitive alternatives that inevitably emerge over time Cultivating and

leveraging a strong brand community that reflects the invisible bond among brand users is one

option The success of firms such as Harley Davidson is based on such brand exploitation

strategies Another option is to create exclusivity by limiting supply and selecting customers A

brand that has implemented such strategy par excellence is the watchmaker Patek Philippe Over

the years Patek Philippe has been able to realize a significant price markup over the already

high-priced products of its competitors A key ingredient to this competitive advantage is the

firmrsquos limited supply and control over access to its watches For its most complex and exclusive

watch the 2001-launched Sky Moon Tourbillon customers need to submit an application to the

CEO including personal information and a motivation letter Patek Philippe produced two


watches a year and set the launch price at euro897130 (Feth 2012) This active supply limitation and

customer selection contributed to maintain the high level of brand passion that spills over across

its entire product portfolio

Communication with the Investor Community

Finally our study offers new tools that support investors and analysts in their work as well as

firms in managing their relations with the investor community For example CEOs and chief

financial officers can improve their communications with investors The key is to understand the

investorsrsquo mental model of how they think about ROIC earnings growth and the sustainability of

excess return and determine whether they see profitability or growth as a top priority for

management The firmrsquos communications can demonstrate how exactly brand investments will

affect future firm value and growth expectations

Our framework can help financial constituencies think differently about their investment

decisions Investors can gain a better understanding of how marketing affects their key metrics

Because our model conceptualizes and quantifies the routes of future cash flow generation

financial analysts may use the empirical estimates as a reference point in their valuation models

Our elasticity estimates are particularly actionable for them

Limitations and Future Research

Our study has limitations that may stimulate further research First our study focuses on one

important market-based asset the brand Although we effectively control for other assets and test

the robustness of our results it would be interesting to study the role of customer satisfaction

service quality and so on in future work Second we use the Harris EquiTrend metric to measure

CBBE which has been done in prior work (eg Bharadwaj et al 2011) Strictly speaking our

results hold true only for this measure There are other CBBE metrics (eg Luo et al 2013 Stahl


et al 2012) and it would be worthwhile to replicate our models with these metrics Finally we

acknowledge that our sample suffers from the same representativeness limitation as previous

research CBBE ratings are predominantly available for larger and more successful brandsfirms

The bias toward larger firms with on average stronger CBBE ratings limits the variation in

our focal CBBE variable Although there is no reason to believe that it affects the

consistency of estimates this bias could reduce the statistical power of our tests Therefore

our findings are rather conservative It would be fruitful to obtain effect estimates in a broader

sample of firms


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Press New York)

Aaker DA (1996) Building Strong Brands (The Free Press New York)

Aaker DA Jacobson R (2001) The value relevance of brand attitude in high-technology markets

J Marketing Res 38(4)485ndash493

Ailawadi KL Lehmann DR Neslin SA (2003) Revenue Premium premium as an Outcome

outcome Measure measure of Brand brand Equityequity J Marketing 67(4)1ndash17

Arellano M (2003) Panel Data Econometrics (Oxford University Press Oxford)

Bharadwaj SG Tuli KR Bonfrer A (2011) The impact of brand quality on shareholder wealth J

Marketing 75 (5)88ndash104

Chaudhuri A Holbrook MB (2001) The chain of effects from brand trust and brand affect to

brand performance The role of brand loyalty J Marketing 65(2)81ndash93

Choi I (2001) Unit root tests for panel data J Int Money and Finance 20249ndash272

Copeland TE Weston JF Shastri K (2013) Financial Theory and Corporate Policy 4th ed

(Pearson Upper Saddle River NJ)

Damodaran A (2012) Investment Valuation Tools and Techniques for Determining the Value of

Any Asset 3rd ed (Wiley Hoboken NJ)

Datta H Ailawadi KL van Heerde HJ (2017) How well does consumer-based brand equity align

with sales-based brand equity and marketing-mix response J Marketing 81(May)1ndash20

Demsetz H (1982) Barriers to entry Am Econ Rev 72(1)47ndash57

Dierickx I Cool K (1989) Asset stock Accumulation and sustainability of competitive advantage

Management Sci 35(12)1504ndash1511

Edeling A Fischer M (2016) Marketings impact on firm value Generalizations from a meta-

analysis J Marketing Res 53(4)515ndash534

Fama EF (1970) Efficient capital markets A review of theory and empirical work J Finance

25(2)383ndash417

Feth GG (2012) Einfach nicht zu haben manager magazin online September 5 2012 [available

at httpwwwmanager-magazindelifestylehardwarea-851706html accessed August 13

2018]

Fischer M Himme A (2017) The financial brand value chain How brand investments contribute

to the financial health of firms Int J Res Marketing 34 (1) 137ndash153

Fischer M Voumllckner F Sattler H (2010) How important are brands A cross-category cross-

country study J Marketing Res 47 (October)823ndash839

Greene WH (2012) Econometric Analysis 7th ed (Pearson Upper Saddle River NJ)

Hanssens DM Parsons LJ Schultz RL (2001) Market Response Models Econometric und Time

Series Analysis 2nd ed (Kluwer Academic Publishers Boston)

Kapferer JN (2011) The New Strategic Brand Management 4th ed (Kogan Page London)


Keller KL (1993) Conceptualizing measuring and managing customer-based brand equity J

Marketing 57(1)1ndash22

Keller KL (2008) Strategic Brand Management Building Measuring and Managing Brand

Equity 3rd ed (Pearson Upper Saddle River NJ)

Keller KL Lehmann DR (2006) Brands and branding Research findings and future priorities

Marketing Sci 25 (6)740ndash759

Koller T Goedhart M Wessels D (2015) Valuation 6th ed (Hoboken NJ Wiley)

Kothari SP (2001) Capital markets research in accounting J Accounting and Economics 31(1ndash

3) 105ndash231

Lane V Jacobson R (1995) Stock market reactions to brand extension announcements The

effects of brand attitude and familiarity J Marketing 59(1)63ndash77

Luo X Raithel S Wiles MA (2013) The impact of brand rating dispersion on firm value J

Marketing Res 50(3) 399ndash415

Makadok R (2001) Toward a synthesis of the resource-based view and dynamic-capability views

of rent creation Strat Management J 22(5)387ndash401

Mela C Gupta S Lehmann DR (1997) The long-term impact of promotion and advertising on

consumer brand choice J Marketing Res 34(2)248ndash261

Mizik N (2014) Assessing the total financial performance impact of brand equity with limited

time-series data J Marketing Res 51(6)691ndash706

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financial implications of shifts in strategic emphasis J Marketing 67(1)63-76

Mizik N Jacobson R (2008) The financial value impact of perceptual brand attributes J

Marketing Res 45(1)15ndash32

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growth Int J Res Marketing 26(4)284ndash293

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brand evaluations J Consumer Res 17263ndash276

Park CS Srinivasan V (1994) A survey-based method for measuring and understanding brand

equity and its extendibility J Marketing Res 31(2)271ndash288

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framework for analysis J Marketing 62(1)2ndash18

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TABLE 1

Empirical Research on the Impact of CBBE on Value Drivers

Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)

Dynamic simultaneous

equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model

Mizik (2014) Financial data

(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice

model linear regression

The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model

Notes CBBE = customer-based brand equity BAV = Brand Asset Valuator

Profitability ROA (return on assets) ROE (return on equity) ROIC (Return on invested capital)

Risk systematic risk idiosyncratic risk credit spread WACC (weighted average cost of capital)


TABLE 2

Brand Value Creation Mechanisms and Their Main Relation(s) to Value Drivers

Most effective for driving hellip

Mechanism of value creation

(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins

Efficiency and effectiveness

of marketing programs

Brand loyalty

Brand extensions

Trade leverage

Competitive advantage

Note The table emphasizes the key relation between a value creation mechanism and a value driver However it

would not be appropriate to conclude that a mechanism is only relevant for its main value driver(s) For example

brand-loyal customers may well contribute to earnings growth by more intensive product use or trying new

products


TABLE 3

Univariate statistics (2005-2013)

N Mean Median Std Dev

Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787

Profitability (ROIC) 4478 22 20 47

Earnings growth (EGR) 3292 13 11 19

Cost of capital (WACC) 3364 09 09 03

Sustainability of excess returns

(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

Advertising expenditures ($m) 2593 55987 16260 110435

Other expenditures ($m) 4472 265816 48286 587054

RampD expenditures ($m) 5517 43557 00 139434

Firm size (ln total assets in $m) 4522 925 915 210

Financial leverage (ratio) 4284 271 111 1425

Industry concentration (ratio) 5517 34 33 14

Investment rate (ratio) 4338 75 91 1257

US GDP growth (ratio) 5517 04 04 02

Operating margin (ratio) 4457 03 14 455

Pretax interest coverage (ratio) 4133 287 07 9306

Dividend payout (ratio) 3882 51 14 611

Asset growth (ratio) 4399 06 04 24

Liquidity (ratio) 3894 172 144 136 1) We calculated sustainability according to Equation 8 using predicted values for ROIC and WACC

from Equations 4 and 6 The sample size of these regressions explains the low number of

observations for sustainability For the univariate statistics we exclude outliers that are more than six

standard deviations away from the mean (32 cases) 74 of 491 cases show an expected duration of 0

years


TABLE 4

Mean Difference Tests

Expected

difference

Observations with

low CBBE

Observations with

high CBBE t-statistic for

difference

N Mean N Mean

Panel A Group split based on median CBBE in total sample

CBBE = 5014 CBBE = 6244

Firm value High gt Low 1128 37058 1186 47182 3397

Profitability (ROIC) High gt Low 1403 22 1393 27 3425

Earnings growth (EGR) High gt Low 1031 12 1152 13 754

Cost of capital (WACC) 954 09 1080 09 1363

Sustainability of excess

return (S)

High gt Low 116 1422 336 1416 026

Panel B Group split based on highest and lowest sample quartiles for CBBE

CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802

Notes The t-test for differences between group means corrects for unequal group variances if necessary

Tests are one-sided if clear directional effects are expected two-sided if not Sample sizes differ depending

on the available observations for focal variables

p lt 01 p lt 05


TABLE 5

IV-Estimation Results for Equations 3ndash6

(First-stage regression)

CBEE (Eq 3) Profitability ROIC

(Eq 4) Earnings growth EGR

(Eq 5) Cost of capital WACC

(Eq 6)

Expected

sign Coefficient

(Standard Error) Expected

sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover

Dependent variable (t ndash 1) + 295 (021) + 324 (001) + 713 (017) + 279 (034)

Marketing constructs IV-CBBE (t ndash 1)

--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)

Estimated SD --- 35x10ndash4 (21x10ndash4) 001 (97 x10ndash5) 1) 631 (106)

Advertising expenditures (t ndash 1) +2) 413 (061) +ndash1) 035 (018) +ndash1) 302 (044) ---

Estimated SD

446 (004)

---

--- ---

Controls

RampD expenditures (t ndash 1) +ndash2) ndash006 (026) +ndash1) ndash056 (012) +ndash1) 166 (035) ---

Other expenditures (t ndash 1) +2) 050 (028) +ndash1) 009 (003) +ndash 1) 002 (010) ---

Operating margin (t ndash 1) + 1316 (389) --- --- ndash ndash028 (008)

Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---

Negative earnings dummy (t ndash 1)

---

--- + 108 (022) ---

Financial leverage (t ndash 1) 1)

--- + 182 (069) +ndash ndash1632 (560) +ndash ndash997 (588)

Profitability (t ndash 1)

---

--- + 037 (045) ---

Investment rate (t ndash 1) 1) --- --- + ndash437 (244) ---

Pretax interest coverage (t ndash 1) 1) --- --- --- - 001 (001)

Dividend payout (t ndash 1)1) --- --- --- + ndash2821 (113)

Asset growth (t ndash 1) --- --- --- ndash 005 (003)

Liquidity (t ndash 1) 1) --- --- --- + 7023 (720)

US GDP growth (t ndash 1) --- +ndash 065 (095) +ndash ndash022 (477) ---

Observed firm and market heterogeneity

Firm size (t ndash 1) +ndash ndash303 (058) +ndash ndash020 (001) ndash1) 2990 (557) ndash ndash003 (001)

Industry concentration (t ndash 1) +ndash ndash2408 (642) +ndash ndash097 (017) +ndash ndash096 (060) +ndash ndash026 (006)

Sample size (Pseudo-R2) 1317 (907) 1084 (867) 979 (667) 649 (652)

Notes Two-sided t-tests Pseudo-R2 measures the squared correlation between actual and predicted values of the dependent variable

p lt 01 p lt 05 p lt 1 1) For reading convenience coefficients are multiplied by 10000 2) Variable is log-transformed


TABLE 6

IV-Estimation Results for Sustainability

of Excess Return S (Equation 7)

Expected

sign

Coefficient (Standard Error)

Intercept 961 (300)

Estimated SD 119 (233)

Marketing constructs

IV-CBBE (t ndash 1) + 011 (004)

Estimated SD 005 (39times10ndash4)

Advertising expenditures (t ndash 1) 1)

+ndash ndash229 (232)

Controls

RampD expenditures (t ndash 1) 1)

+ ndash246 (154)

Other expenditures (t ndash 1) 1)


Asset growth (t ndash 1) + 265 (091)

US GDP growth (t ndash 1) +ndash 796 (880)


Firm size (t ndash 1) +ndash 111 (026)

Industry concentration (t ndash 1) +ndash ndash085 (181)

Weibull scale parameter 1p 460 (013)

Sample size 417

Log likelihood ndash4675

Pseudo-Rsup2 680

Notes Two-sided t-tests p lt 01 p lt 05 p lt 1 1) For reading convenience

coefficients are multiplied by 10000


TABLE 7

Elasticities for Value Drivers and Firm Value by Industry

(Median Based on Distribution of Firm-Specific Elasticity Estimates)

Value-driver elasticity wrt CBBE Firm-value elasticity wrt value driverhellip

NAICS classification

Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)

Automobiles amp components 223 ndash211 1430 211 450

Consumer durables amp apparel 141 1169 1651 183 335

Consumer services 079 3325 1633 205 534

Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467

Food amp staples retailing 228 3830 1020 210 509

Food beverage amp tobacco 249 ndash105 1532 096 281

Household amp personal products 224 185 1646 117 398

Health care 342 1652 1212 079 180

Information technology 276 2532 757 291 140

Telecom services 339 9003 1153 008 020

Transportation 210 5489 1087 537 586

Industrial energy amp utilities 263 5474 1387 435 385

Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373

(standard deviation) 371 6537 701 781 538

Notes Firm-value elasticities for value drivers EGR and S are identical by construction of the valuation formula (see Eq 2)

p lt 01 Note that p-values are not shown for individual industries since the sample sizes are too small for proper statistical inference


TABLE 8

Key Marketing Mix Element for Enhancing Value Drivers


Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting

Communicationadvertising

strategy

Distributionretail strategy

Note The table emphasizes the most powerful marketing mix element to enhance a value driver However it

would not be appropriate to conclude that other mix elements cannot be used to improve a value driver For

example the selection of exclusive retailers may also contribute to raising profit margins


FIGURE 1

Research on the Value Relevance of CBBE

Perceived brand attributes amp

dimensions

Financial value drivers

(ROIC WACC EGR S)

Customer-based brand equity

(CBBE)Firm value

Processes of value creation Processes of value appropriation

Note Bold line shows focus of this study


FIGURE 2


Customer-based brand equity [CBBE ndash eq 3 ]

Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]

Value from value drivers

Earnings growth

[EGR ndash eq 5]

Firm value [FV ndash eq 2]Current earnings

[EBIT(1-τ)]

Not the focus of substantive results but included in the empirical model for completeness and to control for

endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)

Marketing actions (eg advertising)

(+)


FIGURE 3

Value-Based Evaluation of Brand Exploitation Strategy

Importance of value driver for growing firm value

Low High

Leverage effect of CBBE

on value driver

(relative to industry average)

High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4

Value-Based Evaluation of Brand Exploitation Strategy for CVS Health and Kroger (2005ndash2013)


Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of

excess return (S)Profitability

(ROIC)

CVS Health Kroger


APPENDIX

Table A1 Correlation Matrix

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)

2 Profitability (ROIC) 05 100 (2796) (4478)

3 Earnings growth (EGR) 03 40 100

(2183) (3077) (3292)

4 Cost of capital (WACC) 04 02 07 100

(2034) (2998) (2281) (3364)

5 Sustainability (S) 08 ndash12 02 21 100

(452) (491) (491) (485) (491)

6 Advertising expenditures 04 04 03 ndash07 ndash14 100

(1702) (2563) (2055) (1727) (487) (2593)

7 Other expenditures 08 02 ndash05 ndash08 ndash13 40 100 (2794) (4368) (3054) (2986) (487) (2562) (2562)

8 RampD expenditures 01 03 00 ndash03 ndash14 47 35 100

(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)

9 Firm size ndash19 00 ndash06 ndash22 ndash14 43 45 304 100 (2826) (4478) (3085) (3012) (491) (2593) (2593) (4410) (4522)

10 Financial leverage ndash07 01 ndash02 ndash08 00 ndash03 ndash02 ndash02 03 100 (2668) (4268) (2982) (2893) (485) (2399) (2399) (4167) (4284) (4270)

11 Industry concentration ndash01 03 ndash05 ndash04 ndash07 00 07 01 04 ndash01 100

(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)

12 Investment rate 02 00 00 01 13 ndash01 00 00 ndash04 00 01 100

(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)

13 US GDP growth 04 02 03uuml ndash02 07 00 ndash02 ndash01 ndash01 00 05 ndash02 100

(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)

14 Operating margin ndash02 11 ndash01 ndash03 ndash17 03 01 01 07 00 00 00 00 100

(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)

15 Pretax interest coverage 02 014 00 02 01 01 ndash01 00 00 ndash01 00 00 ndash02 00 100

(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)

16 Dividend payout ndash07 00 ndash01 00 06 ndash01 ndash01 ndash01 01 00 01 15 00 00 00 100

(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)

17 Asset growth 02 03 07 02 15 ndash02 00 00 ndash02 ndash01 03 ndash02 11 ndash05 01 01 100

(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)

18 Liquidity 07 ndash05 03 13 04 ndash15 ndash12 ndash01 ndash25 ndash07 ndash06 ndash01 ndash01 ndash10 05 ndash02 07 100

(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)


Table A2 Overview of Symbols

Variables

ADV Advertising expenditures

CBBE Customer-based brand equity

CONC Industry concentration

DIV Dividend payout

EARN Earnings less tax and before interest (net operating profit less tax)

EBIT Earnings before interest and tax

EGR Earnings growth rate

FV Firm value

GDPGR Growth rate of the US gross domestic product

A_GROWTH Asset growth

I Investments in new capital

INT Pretax interest coverage

IR Investment rate

LEV Financial leverage

LIQ Liquidity

D_NEARN Dummy for negative earnings in preceding year (1 = negative)

OE Other expenditures

OPM Operating margin

ROIC Return on invested capital

RD RampD expenditures

S Sustainability of excess returns

SIZE Firm size

WACC Weighted average cost of capital

τ Cash tax rate

Expected realization of a variable

Indexes

i Firm index with i = 1hellip I (number of firms)

t Time index with t = 1hellip T (number of periods)

Model parameters

a b c d g Regression parameters to be estimated

u φ η Error terms

σ2 Variance

ψ Variancendashcovariance matrix of random parameters

f ( ) Density function of expected sustainability of excess returns

λ Location parameter of survival function for

p Scale parameter of survival function for

ε Elasticity


Table A3 Variable Definitions and Measures

Variables Definition Measure Source COMPUSTAT

Firm value (FV) Market capitalization of

equity + preferred stock +

book value of debt +

minority interest

(Yearly average of monthly stock

prices outstanding shares) +

preferred stock + total Liabilities

CRSP (market capitalization equity) +

DATA 10 (preferred stock) DATA 5

(current liabilities) + DATA 9 (long-term

debt) + DATA 49 (minority interest)

Customer-based brand

equity (CBBE)


equity

Survey-based index as measure of

customer-based brand equity (see

Web Appendix W3 for details)

Harris Interactive Poll EquiTrend

Profitability (ROIC) Net operating profit after

tax Invested capital EBITtimes(1- ) Invested capital DATA 308 (operating cash flow) DATA

37 (invested capital)

Earnings growth (EGR) Five-year estimates of

earnings growth

(consensus)

Arithmetic mean across analysts IBES

Earnings (EARN) Net operating profit after

tax EBITtimes(1 ndash ) DATA 308 (operating cash flow)

Cost of capital (WACC) Weighted-average cost of

capital

[Equity times cost of equity + debt

timescost of debttimes(1 ndash τ)]Total capital

Bloomberg

Advertising expenditures

(ADV)

Advertising expenditures -

DATA 45 (advertising)

Other expenditures (OE) Other marketing

expenditures

SGampA expense ndash noncoordinating

costs (advertising RampD bad debt

expense provision for doubtful

accounts employee benefit

expenses)

DATA 189 (SGampA) DATA 45

(advertising) DATA 46 (RampD) DATA

67 (estimated doubtful receivables)

DATA 43 (pensionretirement expense)

DATA 215 (stock options)

RampD expenditures (RD) RampD expenditures - DATA 46 (RampD)

Firm size (SIZE) Total assets Log of total assets DATA 6 (total assets)

Financial Leverage

(LEV)

Book value total debt

Book value equity +

preferred stock

- DATA 5 (current liabilities) + DATA 9

(long-term debt) DATA 60 (common

equity) + DATA 10 (preferred stock)

Industry concentration

(CONC)

Four-firm concentration

index

Cumulative market share of the top

four firms in the industry defined

by two digits of the NAICS

DATA 12 (sales)

Investment rate (IR) (1 ndash cash dividends) Net

operating profit after tax

(1-cash dividends) [EBITtimes

(1 ndash )] DATA 21 (cash dividend) DATA 308

(operating cash flow)

Operating margin

(OPM)

Operating income before

depreciationsales


depreciationsales

DATA 13 (operating income before

depreciation) DATA 12 (sales)

Pretax interest coverage

(INT)

EBIT divided by interest

expense

(Operating income after

depreciation + interest

expense)interest expense

DATA 178 (operating income after

depreciation) DATA 15 (interest

expense)

Dividend payout (DIV) Cash dividendsearnings Cash dividendsavailable income DATA 21 (cash dividend) DATA 20

(income available for common

stockholders)

Asset Growth

(A_GROWTH)

Terminal total

assetsinitial assets

Total assetstotal assetst ndash 1 DATA 6 (total assets)

Liquidity (LIQ) Current ratio Current assetscurrent liabilities DATA 4 (current assets) DATA 5

(current liabilities)

GDPGR US GDP gross rate (Real US GDPt - Real US

GDPtndash1) Real US GDPtndash1

Bureau of Economic Analysis


Report Summary

Recent meta-analysis suggests that on average improving brand equity by 10 translates to an

increase of firm value of 33 Brand-driven value creation arises from growth andor return

relative to capital cost Thus firms may seek to leverage brand strength for driving the profit

margin minus eg through extending the price premium and lowering the cost of selling minus or it may

focus on exploiting brand equity to grow the business minus eg by expanding into new markets

Which strategy is better-suited to drive value creation is not evident Little is known about the

mechanisms that underlie brand-driven value creation and how these might differ across firms

and industries

The Study

In this report Marc Fischer Max Backhaus and Tobias Hornig undertake an approach based on

analysis of financial data to understand the role of customer-based brand equity (CBBE)

measures in creating firm value via three drivers (1) investorsrsquo expectations about the rate of

return on new invested capital (2) the future growth in earnings and (3) the length of the period

during which a firm can earn excess return

They econometrically estimate the CBBE effects on these value drivers at the firm level in a

broad sample of 613 firms covering a period of nine years from 2005 to 2013 Their database

includes retailers durable and non-durable products as well as services

They find that while earnings growth and sustainability of excess return are the most influential

drivers for most industries and firms the return on invested capital is more relevant in industries

such as media information technology and industrial and utilities They also find substantial

across-firm and across-industry variation in both CBBE leverage effects and relative importance

of value drivers Finally they identify cases where CBBE impact on a value driver is not well

aligned with its relevance for value generation leaving an untapped potential for value creation

Put into Practice

Marketers should think differently about their brand strategy a detailed analysis of each firmrsquos

situation is necessary to determine the most effective brand exploitation strategy It is not

sufficient to focus only on raising CBBE by investing into brand building It is equally if not more

important to think about the best brand exploitation strategy so that brand investments are not

wasted but appropriate value in the marketplace

Marc Fischer is Professor of Marketing and Market Research University of Cologne and

Professor of Marketing University of Technology Sydney Max Backhaus is Associate

Researcher University of Cologne Tobias Hornig is Project Manager BI Siemens Industry

Software GmbH
























































the value drivers









































Background



literature

Corporate Valuation

















Industry Interviews














drivers

















































drivers







(1)




FV0

=EBIT

1acute 1-t( ) - I

1

1+WACC( )+


2

1+WACC( )2

+EBIT

3acute 1-t( ) - I

3

1+WACC( )3

+






(2)





































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total































































the value drivers









































Background



literature

Corporate Valuation

















Industry Interviews














drivers

















































drivers







(1)




FV0

=EBIT

1acute 1-t( ) - I

1

1+WACC( )+


2

1+WACC( )2

+EBIT

3acute 1-t( ) - I

3

1+WACC( )3

+






(2)





































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total





























Background



literature

Corporate Valuation

















Industry Interviews














drivers

















































drivers







(1)




FV0

=EBIT

1acute 1-t( ) - I

1

1+WACC( )+


2

1+WACC( )2

+EBIT

3acute 1-t( ) - I

3

1+WACC( )3

+






(2)





































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total



















drivers

















































drivers







(1)




FV0

=EBIT

1acute 1-t( ) - I

1

1+WACC( )+


2

1+WACC( )2

+EBIT

3acute 1-t( ) - I

3

1+WACC( )3

+






(2)





































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total












































drivers







(1)




FV0

=EBIT

1acute 1-t( ) - I

1

1+WACC( )+


2

1+WACC( )2

+EBIT

3acute 1-t( ) - I

3

1+WACC( )3

+






(2)





































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total













(2)





































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total































































































































excess return






















































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total
























































(3)

with












(41)

with



CBBEit

= a0i

+ a1CBBE

it-1+ a

2iln ADV

it-1+ a

3ln RD

it-1+ a

4lnOE

it-1

+a5OPM

it-1+ a

6EARN

it-1+ a

7SIZE

it-1+ a

8CONC

it-1+ u

1it

u

1itsim N 0s

u1

2( ) a i= a + Y

aw

ai and Var a

i( ) = Ya

centYa

a

u

2itsim N 0s

u2

2( ) bi= b + Y

bw

bi and Var b

i( ) = Yb

centYb




(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total











(42)




(5)

with









(61)

with

u

3itsim N 0s

iu3

2( ) ci= c + Y

cw

ci and Var c

i( ) = Yc

centYc

u

4itsim N 0s

u4

2( ) di= d + Y

dw

di and Var d

i( ) = Yd

centYd





(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total












(62)














(7)

g

i= g + Y

gw

gi and Var g

i( ) = Yg

centYg








Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total















Data and Estimation

Data Sources

































(8)






information

































Estimation































Empirical Results
























a

2= 413

b

2= 001

c

2= 003






firms

















deviation

d

2= 1109

g

1= 011

























significant for S









section

Robustness Checks










sample


























average5




decision making



























consider here























































marketing mix















































competitive market










































































sample of firms


References


Press New York)
























25(2)383ndash417



2018]


















3) 105ndash231






















New York)






95 493ndash507











184ndash197






TABLE 1


Value Drivers

Reference Data CBBE

Metric Model

Profit-

ability Risk

Earnings

Growth

Sustainability

of Excess

Return

Firm

Industry

Level

Aaker and

Jacobson

(2001)

Financial data

(1988ndash1994)

Index

(proprietary)

Linear regression

Bharadwaj et al

(2011)

Financial data

(2000ndash2005)

Dimensions

(EquiTrend)

Linear regression

Fischer and

Himme (2017)

Financial data

(2005ndash2012)

Index

(EquiTrend)


equation system

Luo et al

(2013)

Financial data

(2008ndash2011)

Index

(YouGov)

VAR model


(2000ndash2010)

Index

(BAV)

Linear regression

Rego et al

(2009)

Financial data

(2000ndash2006)

Index

(EquiTrend)

Linear regression

ordered logit model

Stahl et al

(2012)

Company and

customer data

(1998ndash2008)

Dimensions

(BAV)

(Aggregate) choice


The current

study

Financial data

(2005ndash2013)

Index

(EquiTrend)

Linear regression

hazard model





TABLE 2




(Aaker 1991)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Prices and margins



Brand loyalty

Brand extensions

Trade leverage





products


TABLE 3



Firm value ($m) 3588 3207490 1041969 6145674

CBBE (0ndash100) 3289 5629 5668 787





(years) 1)

491 1404 839 1848

EBIT (1 ndash τ) ($m) 4514 288366 82650 673035

















years


TABLE 4


Expected

difference

Observations with

low CBBE

Observations with


difference

N Mean N Mean


CBBE = 5014 CBBE = 6244






return (S)

High gt Low 116 1422 336 1416 026


CBBE = 4603 CBBE = 6556






return (S)

High gt Low 46 1064 185 1541 1802




p lt 01 p lt 05


TABLE 5






(Eq 6)

Expected

sign Coefficient


sign Coefficient


sign Coefficient


sign Coefficient

(Standard Error)

Intercept 50639 (109) 426 (022) 001 (083) 128 (008)

Estimated SD

833 (073)

113 (012)

515 (061)

002 (006)

Carryover



--- + 001 (27x10ndash4) + 003 (001) +ndash 1) 1109 (984)



Estimated SD

446 (004)

---

--- ---

Controls




Earnings (t ndash 1) 1) + 1345 (389)

--- +ndash ndash005 (002) ---


---

--- + 108 (022) ---




---

--- + 037 (045) ---














TABLE 6



Expected

sign


Intercept 961 (300)



IV-CBBE (t ndash 1) + 011 (004)




Controls


+ ndash246 (154)









Sample size 417


Pseudo-Rsup2 680




TABLE 7





Profitability

(ROIC)

Earnings

growth (EGR)

Sustainability

of excess

return (S)

Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability

of excess

return (S)




Media 116 1541 1395 575 356

Retailing 169 3556 1135 257 467




Health care 342 1652 1212 079 180



Transportation 210 5489 1087 537 586


Overall (median) 205 3014 1243 180 353

(mean) 172 1776 1220 390 373





TABLE 8



Profitability

(ROIC)

Earnings

growth

(EGR)

Sustainability of

excess return

(S)

Productinnovation

Pricecontracting


strategy






FIGURE 1



dimensions


(ROIC WACC EGR S)


(CBBE)Firm value




FIGURE 2



Profitability

[ROIC ndash eq 41]

Sustainability of

excess return

[S ndash eq 7]


Earnings growth

[EGR ndash eq 5]


[EBIT(1-τ)]


endogeneity

(+) (+)(+)

Cost of capital

[WACC ndash eq 61]

(+-)


(+)


FIGURE 3



Low High


on value driver


High

Low

Wasted

resources on

brand leverage

Appropriate

brand leverage

Refocus of brand

exploitation

strategy necessary

Appropriate

brand leverage


FIGURE 4



Low High

Leverage effect

of CBBE

on value driver

(relative to

industry average)

High

Low

High

Low

Low High

Earnings growth

(EGR)

Sustainability of

excess return (S)

Profitability

(ROIC)

Earnings growth

(EGR)

Sustainability of


(ROIC)

CVS Health Kroger


APPENDIX


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 CBBE 100

(3289)



(2183) (3077) (3292)


(2034) (2998) (2281) (3364)


(452) (491) (491) (485) (491)


(1702) (2563) (2055) (1727) (487) (2593)



(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472)




(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284)


(2753) (4295) (3071) (2929) (491) (2589) (2589) (4232) (4338) (4336) (4091) (4338)


(3289) (4478) (3292) (3364) (491) (2593) (2593) (4472) (5517) (4522) (4284) (5517) (4338)


(2796) (4412) (3044) (2987) (491) (2592) (2592) (4358) (4457) (4456) (4205) (4457) (4279) (4457)


(2607) (4089) (2789) (2742) (488) (2282) (2282) (4037) (4133) (4130) (3879) (4133) (3958) (4083) (4133)


(2515) (3839) (3022) (2636) (490) (2583) (2583) (3778) (3882) (3880) (3643) (3882) (3878) (3823) (3507) (3882)


(2758) (4362) (3033) (2948) (491) (2511) (2511) (4294) (4399) (4399) (4164) (4399) (4224) (4335) (4021) (3770) (4399)


(2458) (3851) (2561) (2588) (491) (2299) (2299) (3851) (3894) (3892) (3666) (3894) (3714) (3886) (3720) (3266) (3779) (3894)



Variables




DIV Dividend payout




FV Firm value





IR Investment rate


LIQ Liquidity







SIZE Firm size


τ Cash tax rate


Indexes



Model parameters


u φ η Error terms

σ2 Variance





ε Elasticity







minority interest









equity (CBBE)


equity









earnings growth

(consensus)





capital



Bloomberg


(ADV)




expenditures





expenses)








Financial Leverage

(LEV)


Book value equity +

preferred stock





(CONC)


index




DATA 12 (sales)






Operating margin

(OPM)


depreciationsales


depreciationsales




(INT)


expense






expense)



stockholders)

Asset Growth

(A_GROWTH)

Terminal total









value-based brand exploitation strategy to grow firm value

Documents